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Use this skill whenever the user mentions a PDF file or asks to produce/edit one. For read-only tasks such as reading, summarizing, extracting plain text, or answering questions from a PDF, follow this skill's read-only routing rules: use the built-in Read tool first, do not write code or scripts, and prefer markitdown for PDFs over 100 pages. Use PDF processing libraries/scripts only for modification tasks such as merging, splitting, rotating, watermarking, filling forms, encrypting/decrypting, extracting images, OCR, or creating PDFs.

proma-ai By proma-ai schedule Updated 6/14/2026

name: pdf description: Use this skill whenever the user mentions a PDF file or asks to produce/edit one. For read-only tasks such as reading, summarizing, extracting plain text, or answering questions from a PDF, follow this skill's read-only routing rules: use the built-in Read tool first, do not write code or scripts, and prefer markitdown for PDFs over 100 pages. Use PDF processing libraries/scripts only for modification tasks such as merging, splitting, rotating, watermarking, filling forms, encrypting/decrypting, extracting images, OCR, or creating PDFs. license: Proprietary. LICENSE.txt has complete terms version: "1.0.4"

PDF Processing Guide

Overview

This guide defines how to handle PDF files. Read-only tasks must be handled with built-in tools first. Python libraries and scripts are fallback tools for PDF modification, OCR, form filling, or other operations that cannot be completed by direct reading.

Read-Only Routing Rules

Use this section for requests like "read this PDF", "summarize this PDF", "answer questions from this PDF", or "extract the main points".

  1. Prefer the built-in Read tool on the PDF path.

    • Do not write Python, JavaScript, shell scripts, or temporary extraction files for simple reading.
    • Do not use the Python examples below for read-only tasks unless the built-in Read tool fails or the user explicitly asks for a generated file.
  2. If the PDF has more than 100 pages, prefer markitdown before Python libraries.

    • First check whether a global markitdown command is available.
    • If global markitdown exists, use it directly.
    • If global markitdown is missing, install it globally for the user, then use it.
    • Do not create wrapper scripts around markitdown.
    • If installation fails because of network, permissions, or missing Python tooling, say so briefly and fall back to the built-in Read tool or ask which page range to inspect.
  3. Only move to PDF processing libraries or scripts when the task needs document transformation, complex table extraction, OCR, form filling, image extraction, or PDF generation.

Page Count Check

Use the cheapest available command. Try pdfinfo first:

pdfinfo input.pdf | grep '^Pages:'

If pdfinfo is unavailable, use the built-in Read tool and infer whether the document is long from the tool result. Avoid writing a custom page-count script just to decide the reading path.

markitdown for Long PDFs

For PDFs over 100 pages, first check for a globally available markitdown command:

command -v markitdown

If it exists, convert to Markdown directly:

markitdown input.pdf

If markitdown is not installed, install it globally before converting. Prefer direct pip installation because it is usually faster and has fewer network/toolchain dependencies than Homebrew-based flows.

Before installing, quickly verify the package source/version if tooling is available:

python3 -m pip index versions markitdown

Then install:

python3 -m pip install --user "markitdown[all]"

After installation, run markitdown input.pdf. If the command is not on PATH, try python3 -m markitdown input.pdf or use the user's Python user-base bin path.

If the output is too long for the response, inspect or summarize relevant sections instead of dumping the full text. When saving a converted Markdown file is useful, ask only if the user did not already request a file output.

Modification and Advanced Processing

Use the sections below when the user asks to modify PDFs, create PDFs, fill forms, extract images, OCR scanned pages, or perform precise table extraction that the built-in Read tool cannot handle.

Python Libraries

pypdf - Basic Operations

Merge PDFs

from pypdf import PdfWriter, PdfReader

writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

with open("merged.pdf", "wb") as output:
    writer.write(output)

Split PDF

reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
    writer = PdfWriter()
    writer.add_page(page)
    with open(f"page_{i+1}.pdf", "wb") as output:
        writer.write(output)

Extract Metadata

reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
print(f"Subject: {meta.subject}")
print(f"Creator: {meta.creator}")

Rotate Pages

reader = PdfReader("input.pdf")
writer = PdfWriter()

page = reader.pages[0]
page.rotate(90)  # Rotate 90 degrees clockwise
writer.add_page(page)

with open("rotated.pdf", "wb") as output:
    writer.write(output)

pdfplumber - Text and Table Extraction

Extract Text with Layout

import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        print(text)

Extract Tables

with pdfplumber.open("document.pdf") as pdf:
    for i, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for j, table in enumerate(tables):
            print(f"Table {j+1} on page {i+1}:")
            for row in table:
                print(row)

Advanced Table Extraction

import pandas as pd

with pdfplumber.open("document.pdf") as pdf:
    all_tables = []
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            if table:  # Check if table is not empty
                df = pd.DataFrame(table[1:], columns=table[0])
                all_tables.append(df)

# Combine all tables
if all_tables:
    combined_df = pd.concat(all_tables, ignore_index=True)
    combined_df.to_excel("extracted_tables.xlsx", index=False)

reportlab - Create PDFs

Basic PDF Creation

from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("hello.pdf", pagesize=letter)
width, height = letter

# Add text
c.drawString(100, height - 100, "Hello World!")
c.drawString(100, height - 120, "This is a PDF created with reportlab")

# Add a line
c.line(100, height - 140, 400, height - 140)

# Save
c.save()

Create PDF with Multiple Pages

from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet

doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []

# Add content
title = Paragraph("Report Title", styles['Title'])
story.append(title)
story.append(Spacer(1, 12))

body = Paragraph("This is the body of the report. " * 20, styles['Normal'])
story.append(body)
story.append(PageBreak())

# Page 2
story.append(Paragraph("Page 2", styles['Heading1']))
story.append(Paragraph("Content for page 2", styles['Normal']))

# Build PDF
doc.build(story)

Subscripts and Superscripts

IMPORTANT: Never use Unicode subscript/superscript characters (₀₁₂₃₄₅₆₇₈₉, ⁰¹²³⁴⁵⁶⁷⁸⁹) in ReportLab PDFs. The built-in fonts do not include these glyphs, causing them to render as solid black boxes.

Instead, use ReportLab's XML markup tags in Paragraph objects:

from reportlab.platypus import Paragraph
from reportlab.lib.styles import getSampleStyleSheet

styles = getSampleStyleSheet()

# Subscripts: use <sub> tag
chemical = Paragraph("H<sub>2</sub>O", styles['Normal'])

# Superscripts: use <super> tag
squared = Paragraph("x<super>2</super> + y<super>2</super>", styles['Normal'])

For canvas-drawn text (not Paragraph objects), manually adjust font the size and position rather than using Unicode subscripts/superscripts.

Command-Line Tools

pdftotext (poppler-utils)

# Extract text
pdftotext input.pdf output.txt

# Extract text preserving layout
pdftotext -layout input.pdf output.txt

# Extract specific pages
pdftotext -f 1 -l 5 input.pdf output.txt  # Pages 1-5

qpdf

# Merge PDFs
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf

# Split pages
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
qpdf input.pdf --pages . 6-10 -- pages6-10.pdf

# Rotate pages
qpdf input.pdf output.pdf --rotate=+90:1  # Rotate page 1 by 90 degrees

# Remove password
qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf

pdftk (if available)

# Merge
pdftk file1.pdf file2.pdf cat output merged.pdf

# Split
pdftk input.pdf burst

# Rotate
pdftk input.pdf rotate 1east output rotated.pdf

Common Tasks

Extract Text from Scanned PDFs

# Requires: pip install pytesseract pdf2image
import pytesseract
from pdf2image import convert_from_path

# Convert PDF to images
images = convert_from_path('scanned.pdf')

# OCR each page
text = ""
for i, image in enumerate(images):
    text += f"Page {i+1}:\n"
    text += pytesseract.image_to_string(image)
    text += "\n\n"

print(text)

Add Watermark

from pypdf import PdfReader, PdfWriter

# Create watermark (or load existing)
watermark = PdfReader("watermark.pdf").pages[0]

# Apply to all pages
reader = PdfReader("document.pdf")
writer = PdfWriter()

for page in reader.pages:
    page.merge_page(watermark)
    writer.add_page(page)

with open("watermarked.pdf", "wb") as output:
    writer.write(output)

Extract Images

# Using pdfimages (poppler-utils)
pdfimages -j input.pdf output_prefix

# This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.

Password Protection

from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()

for page in reader.pages:
    writer.add_page(page)

# Add password
writer.encrypt("userpassword", "ownerpassword")

with open("encrypted.pdf", "wb") as output:
    writer.write(output)

Quick Reference

Task Best Tool Command/Code
Merge PDFs pypdf writer.add_page(page)
Split PDFs pypdf One page per file
Read or summarize PDFs Built-in Read tool Use Read first; no scripts
Long PDF text extraction (>100 pages) markitdown markitdown input.pdf
Extract text after Read fails pdfplumber page.extract_text()
Extract tables after Read fails pdfplumber page.extract_tables()
Create PDFs reportlab Canvas or Platypus
Command line merge qpdf qpdf --empty --pages ...
OCR scanned PDFs pytesseract Convert to image first
Fill PDF forms pdf-lib or pypdf (see FORMS.md) See FORMS.md

Next Steps

  • For advanced pypdfium2 usage, see REFERENCE.md
  • For JavaScript libraries (pdf-lib), see REFERENCE.md
  • If you need to fill out a PDF form, follow the instructions in FORMS.md
  • For troubleshooting guides, see REFERENCE.md
Install via CLI
npx skills add https://github.com/proma-ai/Proma --skill pdf
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